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Creators/Authors contains: "Zhang, R."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Geophysical Research: Atmospheres)
    Free, publicly-accessible full text available December 16, 2022
  2. ; ; ; ( , International Conference on Autonomous Agents and Multiagent Systems)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Applied Optics)
    Free, publicly-accessible full text available July 8, 2022
  4. ; ; ( , International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; ( , International Joint Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; ; ; ; et al ( , Computing and Software for Big Science)
    Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hardmore »scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.« less
    Free, publicly-accessible full text available December 1, 2023
  7. ; ( , Global journal of advanced engineering technologies and sciences)
    Building energy modeling and simulation is an effective approach to evaluate building performance and energy system operations to achieve higher building energy efficiency. The high-order building models can offer exceptional simulation capacity and accuracy, however, its high level of complexity does not allow it to directly work with the optimization algorithms and methods that require a complete differential-algebraic-equations-based mathematical description of the physical model. In order to fill in the gap, the study presents a systematic approach to develop and calibrate the reduced-order building models. A notable feature of the approach is its coupling with high-order building simulations in ordermore »to pre-process the input information and support the calibration of the reduced model. A case study on a representative office building shows that the developed reduced-order model can present acceptable simulation accuracy compared with high-order simulations and significantly reduce the modeling complexity.« less
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Angewandte Chemie)
    How bio-membranes are self-organized to perform their functions remains a pivotal issue in biological and chemical science. Understanding the self-assembly principles of lipid-like molecules hence becomes crucial. Here we report the meso-structural evolution of amphiphilic sphere-rod conjugates (giant lipids), and study the roles of geometric parameters (head-tail ratio and cross-section area) during this course. As a prototype system, giant lipids resemble natural lipidic molecules by capturing their essential features including head-tail configuration, monodispersed molecular weight distribution and minor interpenetration of hydrophobic tails. We demonstrate the self-assembly behavior of two categories of giant lipids (I-shape and T-shape, a total of 8more »molecules). A rich variety of meso-structures are constructed in solution state and their molecular packing models are rationally understood. We streamline the driving forces of morphological evolution from both geometric and thermodynamic perspective. Giant lipids recast the phase behavior of both linear and branched lipidic molecules to certain degree, while the abundant self-assembled morphologies reveal distinct physiochemical behaviors when geometric parameters deviate from natural analogues.« less
    Full Text Available 
  9. ; ( , EG-ICE 2020 Workshop)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))
    Accepted Manuscript Full Text Available